2020
DOI: 10.1016/j.agrformet.2019.107851
|View full text |Cite
|
Sign up to set email alerts
|

Why do crop models diverge substantially in climate impact projections? A comprehensive analysis based on eight barley crop models

Abstract: Robust projections of climate impact on crop growth and productivity by crop models are key to designing effective adaptations to cope with future climate risk. However, current crop models diverge strongly in their climate impact projections. Previous studies tried to compare or improve crop models regarding the impact of one single climate variable. However, this approach is insufficient, considering that crop growth and yield are affected by the interactive impacts of multiple climate change factors and mul… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
34
0
1

Year Published

2020
2020
2024
2024

Publication Types

Select...
6
2
1

Relationship

2
7

Authors

Journals

citations
Cited by 44 publications
(35 citation statements)
references
References 76 publications
0
34
0
1
Order By: Relevance
“…The capacity of crop models to account for source–sink manipulation is therefore questionable (Asseng et al ., 2017). A recent study evaluated why several crop models diverge in their prediction of barley production in response to climate change, particularly at high temperatures and elevated CO 2 (Tao et al ., 2020). The difference originated from models limiting growth directly vs others that used indirect limitation (e.g.…”
Section: The Why: the Importance Of Measuring Different Facets Of Plant Growthmentioning
confidence: 99%
“…The capacity of crop models to account for source–sink manipulation is therefore questionable (Asseng et al ., 2017). A recent study evaluated why several crop models diverge in their prediction of barley production in response to climate change, particularly at high temperatures and elevated CO 2 (Tao et al ., 2020). The difference originated from models limiting growth directly vs others that used indirect limitation (e.g.…”
Section: The Why: the Importance Of Measuring Different Facets Of Plant Growthmentioning
confidence: 99%
“…Crop model intercomparisons have proven useful to compare consistency among models and quantify uncertainty in model predictions (Asseng et al., 2013; Bassu et al., 2014; Fleisher et al., 2017; Li et al., 2015; Ruane et al., 2017). They have reinforced the benefit of multimodel approaches, as they help identify sources of uncertainty (associated with model parameters, model structure and model users; Tao et al., 2018, 2020). The ensemble mean or median usually resulted as best predictors for multiple crops and for different soil and plant variables (Martre et al., 2015; Wallach et al., 2018).…”
Section: Introductionmentioning
confidence: 99%
“…Conversely, the northward shift of suitable area should make the northern cluster more apt for wheat production, without changing sowing dates 78 . Moreover, it is relevant to stress that projections of wheat and barley yields to the mid-of-century period entail uncertainties due to multiple interrelated biophysical processes 58 . The use of multi-model ensembles should be seen as a tool to reduce these uncertainties.…”
Section: Discussionmentioning
confidence: 99%